1,3-butadiene (BD) is a common air pollutant released by gasoline, cigarette smoke, and automobile emissions. In humans, BD is oxidized by P450 enzymes (CYP2E1, 2E1) to epoxybutene, epoxybutanediol and diepoxybutane(?), which are deactivated by polymorphic epoxide hydrolase (EH). BD is classified as a suspect human carcinogen. BD is enzymatically activated and deactivated, so genetically based differences in susceptibility are expected. Three types of biomarkers show promise for estimating internal dose of epoxybutene and epoxybutanediol; hemoglobin N-terminal valine adducts, lymphocyte 7- alkylguanine DNA adducts, and the urinary mercapturic acid of butenediol. A multicenter collaboration is proposed to quantity exposure-dose relationships for each of these biomarkers of dose. This relationship is defined by a biomarker's background levels, slope, precision, biases and interferences. This relationship is affected by an individual's 2E1 phenotype, EH genotype, and lifestyle factors, which will be assessed. Eighty (80) subjects will be drawn from male Taiwanese petrochemical workers producing butadiene latex and related products at 10 large plants (250 workers total). Subjects will be selected as 4 activation/deactivation subgroups of 20 subjects with either fast or slow 2E1 activation rates, and normal or variant EH genotypes. Eighty (80) controls, age and metabolism matched to the exposed, will be drawn from local unexposed workers. Preliminary sampling showed two types of exposures: high intensity (greater than 100 ppm average for 2-3 hr.) during tank cleaning, and long-term low level (medians 0.5-2 ppm full shift) during normal operations, and little concurrent exposure to styrene or acrylonitrite. Twenty tank cleaners will provide data on high exposure responses. Long-term exposures (5 wks) will be monitored for the 80 operators. Blood and urine samples will be collected before, after 3 wks, and after 5 wks to determine biomarker levels and their changes during the measured exposure. Exposure-biomarkers relationships will be analyzed by regression techniques to see effects of metabolism and personal factors. Simulations showed the strategy estimated slopes to q20 percent (95 percent CI) for an oxidation-deactivation subgroup of 20, and had 80 percent power (p less than 0.05) to detect a q10 percent difference in slopes among subgroups. Biomarkers will be compared for background levels, biases and sensitivity, and ease of sample collection and analysis.